Workflows for Reproducible Research with R & Git

.title[
# Workflows for Reproducible Research with R & Git
]
.subtitle[
## Dependency Management
]
.author[
### Johannes Breuer, Bernd Weiss, & Arnim Bleier
]
.date[
### 2023-11-17
]

---

---

## Dependencies in `R`

Most `R` packages depend on other `R` packages.

All `R` packages depend on `R`.

Both `R` and `R` packages have versions.

Different versions of `R` packages may depend on different versions of `R` and different versions of other packages.

---

## Dependencies in `R`

---

## Dependencies in `R`

<img src="data:image/png;base64,#../img/cran_usethis.png" width="175%" style="display: block; margin: auto;" />
Source: https://cran.r-project.org/web/packages/usethis/index.html

---

["It's ~~turtles~~ software all the way down"](https://en.wikipedia.org/wiki/Turtles_all_the_way_down)

🐢

... or is it???

---

## Digging ⛏ into your tool stack 🛠

Your full `R` setup consists of:

1. Specific versions of `R` packages1
2. A specific version of `R`2
3. A specific version of your operating system
4. Specific hardware

[2] You can also have different versions of `R` installed on your machine.
]

---

## Further dependencies

*Note*: We could also add system libraries between `R` and the OS (which are especially relevant in the [Linux/Unix world](https://www.tutorialspoint.com/operating_system/os_linux.htm)).

---

## The danger of dependencies

<img src="data:image/png;base64,#https://imgs.xkcd.com/comics/dependency.png" width="50%" style="display: block; margin: auto;" />
https://xkcd.com/2347/

---

## What to "ship" 🚢?

- `R` code (+ underlying data)
  - this should include information about the packages used
  
- information about the version of `R` and the used packages

- your whole computational environment (focus of the next session)

- overall goal: preventing what is known as "code rot" & "works-on-my-machine errors" (WOMME)

---

## Dependency management solutions

As with almost everything in the `R` ecosystem, there are multiple solutions for dependency management:

- a manual approach
- [~~`checkpoint`~~](https://github.com/RevolutionAnalytics/checkpoint)1
- [**`groundhog`**](https://groundhogr.com/)
- [`renv`](https://rstudio.github.io/renv/)2
- [`rang`](https://github.com/gesistsa/rang)3

.small[
[1] Not an option anymore as it relied on the *CRAN Time Machine snapshots* from the *Microsoft R Application Network* (MRAN) which was [retired in July 2022](https://techcommunity.microsoft.com/t5/azure-sql-blog/microsoft-r-application-network-retirement/ba-p/3707161).

[2] `renv` is the successor of [`packrat`](https://github.com/rstudio/packrat) (which is not maintained anymore).

[3] Developed by members of the team [Transparent Social Analytics](https://www.gesis.org/en/institute/staff/orga/tiles/5/74?cHash=8fbd330b798c8dd7cb84097ddfd82054) at GESIS.
]

---

## Manual approach to dependency management

There is an easy-to-use manual solution for providing information about the packages and `R` version used in your project:

```r
sessionInfo()
```

```
## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=German_Germany.utf8  LC_CTYPE=German_Germany.utf8    LC_MONETARY=German_Germany.utf8
## [4] LC_NUMERIC=C                    LC_TIME=German_Germany.utf8    
## 
## time zone: Europe/Berlin
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] depgraph_0.1.0    emo_0.0.0.9000    lubridate_1.9.3   forcats_1.0.0     stringr_1.5.0     purrr_1.0.2      
##  [7] readr_2.1.4       tidyr_1.3.0       tibble_3.2.1      tidyverse_2.0.0   knitr_1.44        sjPlot_2.8.15    
## [13] scales_1.2.1      ggplot2_3.4.3     correlation_0.8.4 dplyr_1.1.3      
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3  rstudioapi_0.15.0   jsonlite_1.8.7      datawizard_0.9.0    magrittr_2.0.3     
##   [6] TH.data_1.1-2       estimability_1.4.1  farver_2.1.1        nloptr_2.0.3        rmarkdown_2.25     
##  [11] fs_1.6.3            vctrs_0.6.3         memoise_2.0.1       minqa_1.2.6         effectsize_0.8.6   
##  [16] webshot_0.5.5       htmltools_0.5.6.1   usethis_2.2.2       broom_1.0.5         sjmisc_2.8.9       
##  [21] sass_0.4.7          xaringan_0.28       bslib_0.5.1         fontawesome_0.5.2   htmlwidgets_1.6.2  
##  [26] sandwich_3.0-2      emmeans_1.8.8       zoo_1.8-12          cachem_1.0.8        uuid_1.1-1         
##  [31] igraph_1.5.1        ggnetwork_0.5.12    mime_0.12           lifecycle_1.0.3     woRkshoptools_0.1.0
##  [36] pkgconfig_2.0.3     sjlabelled_1.2.0    Matrix_1.6-1.1      R6_2.5.1            fastmap_1.1.1      
##  [41] shiny_1.7.5         snakecase_0.11.1    digest_0.6.33       easypackages_0.1.0  colorspace_2.1-0   
##  [46] ps_1.7.5            pkgload_1.3.3       labeling_0.4.3      timechange_0.2.0    fansi_1.0.5        
##  [51] httr_1.4.7          compiler_4.3.2      remotes_2.4.2.1     bit64_4.0.5         withr_2.5.1        
##  [56] backports_1.4.1     performance_0.10.5  highr_0.10          pkgbuild_1.4.2      MASS_7.3-60        
##  [61] sjstats_0.18.2      sessioninfo_1.2.2   miniCRAN_0.2.16     tools_4.3.2         httpuv_1.6.11      
##  [66] glue_1.6.2          callr_3.7.3         nlme_3.1-163        promises_1.2.1      grid_4.3.2         
##  [71] generics_0.1.3      gtable_0.3.4        tzdb_0.4.0          hms_1.1.3           xml2_1.3.5         
##  [76] utf8_1.2.3          ggrepel_0.9.3       pillar_1.9.0        vroom_1.6.4         later_1.3.1        
##  [81] splines_4.3.2       lattice_0.21-9      survival_3.5-7      bit_4.0.5           tidyselect_1.2.0   
##  [86] miniUI_0.1.1.1      svglite_2.1.2       xfun_0.40           devtools_2.4.5      stringi_1.7.12     
##  [91] yaml_2.3.7          boot_1.3-28.1       xaringanExtra_0.7.0 kableExtra_1.3.4    evaluate_0.22      
##  [96] codetools_0.2-19    cli_3.6.1           xtable_1.8-4        parameters_0.21.2   systemfonts_1.0.5  
## [101] munsell_0.5.0       processx_3.8.2      jquerylib_0.1.4     modelr_0.1.11       Rcpp_1.0.11        
## [106] ggeffects_1.3.1     coda_0.19-4         parallel_4.3.2      ellipsis_0.3.2      assertthat_0.2.1   
## [111] prettyunits_1.2.0   bayestestR_0.13.1   profvis_0.3.8       urlchecker_1.0.1    lme4_1.1-34        
## [116] viridisLite_0.4.2   mvtnorm_1.2-3       insight_0.19.5      crayon_1.5.2        rlang_1.1.1        
## [121] rvest_1.0.3         multcomp_1.4-25
```
]

---

## `groundhog`

[`groundhog`](https://groundhogr.com/) is a lightweight package that allows you to increase the reproducibility of your `R` scripts. It does so by installing and loading "packages & their dependencies as available on chosen date on CRAN".

---

## Using `groundhog`

All you need to do to use `groundhog` is specifying the packages you want to use in your script and a date.

```r
install.packages("groundhog")
library(groundhog)

pkgs <- c("tidyverse", "janitor", "sjPlot")

groundhog.library(pkgs, date = "2023-11-71")
```

---

## How `groundhog` works

From the [package website](https://groundhogr.com/back-end/): "groundhog relies on a database that contains virtually all package versions ever uploaded to CRAN, the date when they were published, and all dependencies."

`groundhog` also used to rely on MRAN. Since that has been retired, however, it now uses its own package repository: [GRAN: Groundhog R Archive Neighbor](https://groundhogr.com/gran/).

---

## How `groundhog` works

`groundhog` uses its own package library to install the packages you specified. From the documentation of the `restore.library()` function: "When groundhog installs a package, it installs it into groundhog's library."

```r
library(groundhog)
get.groundhog.folder()
```

```
## [1] "C:/Users/breuerjs/Documents/R_groundhog/groundhog_library/"
```

"Groundhog then immediately moves the installed package(s) (and their dependencies) to the default personal library."

```r
.libPaths()[1]
```

```
## [1] "C:/Users/breuerjs/AppData/Local/R/win-library/4.3"
```

.small[
*Note*: You can find a lot more technical information on the [`groundhog` website](https://groundhogr.com/) and within the help files for the `groundhog` functions.
]

---

## Reversing changes made by `groundhog`

You can reverse the changes made by `groundhog` to your default personal `R` package library:

```r
restore.library()
```
---

## Pros and cons of `groundhog`

*Pros:*
- can be easily used to make existing `R` scripts more reproducible
- does not require a "project-based workflow"1
- does not require any specific knowledge on the reproducer's side

*Cons:*
- limited to packages from CRAN
- works with package snapshots from specific dates, not with specific package versions
  - in reality, our installed packages are very often not up-to-date
- installs specific package versions, but not specific versions of `R`

.small[
[1] While you as someone who highly values reproducibility, of course, do use a project-based workflow, the people who want to reproduce your analysis might not 😉
]

---

## Choosing a date

The recommendation by the `groundhog` package authors for choosing a date is that "a good default is the first day of the month when starting your project". Once you have added `groundhog.library()` to your script, re-run it to make sure it produces the expected results.

You can also update all of the packages you use and then specify the current date as the date for `groundhog.library()`.

---

## `groundhog` and `R` versions

The `groundhog` database also includes information on base `R` releases. As `groundhog` does not install specific versions of `R`, you should still specify the version of `R` you used in your script.

```r
R.version.string
```

```
## [1] "R version 4.3.2 (2023-10-31 ucrt)"
```

You can find the release dates for all versions of R via the [CRAN archive page](https://cran.r-project.org/src/base/).

---

## Excursus: Updating `R`

As you probably know, you can download the most recent version of `R` from the [CRAN website](https://cran.r-project.org/). You can download older versions of `R` via the CRAN archive packages for [*Windows*](https://cran.r-project.org/bin/windows/base/old/) and [*Mac OS X*](https://cran-archive.r-project.org/bin/macosx/).1

.small[
[1] How you install and update `R` on Linux depends on your [distribution](https://cran.r-project.org/bin/linux/).
]

---

## Excursus: Updating `R`

On Windows, you can also use the [`installr` package](https://talgalili.github.io/installr/) to update `R`.1

```r
install.packages("installr")
library(installr)

updateR()
```

.small[
[1] The `installr` package also offers some interesting other functionalities, such as installing `Git` via the `install.git()` function.
]

---

## Excursus: Updating packages

The easiest way of updating packages is simply using `install.packages()`. To update all packages or a specific set of packages, you can use the `update.packages()` function.

```r
# update all installed packages
update.packages()

# update specific packages
update.packages(oldPkgs = c("tidyverse", "janitor", "sjPlot"))
```
---

## Excursus: Updating packages

With the following code, you can detach and update all of the packages you have currently loaded in your `R` session (excluding core `R` packages):

```r
loaded_pkgs <- search()
loaded_pkgs <- loaded_pkgs[grep("^package:", loaded_pkgs)]

exclude_pkgs <- c("package:base", "package:stats", "package:graphics", "package:grDevices",
 "package:utils", "package:datasets", "package:methods", "package:utils")
loaded_pkgs <- loaded_pkgs[!loaded_pkgs %in% exclude_pkgs]

for (pkg in loaded_pkgs) {
  detach(pkg, character.only = TRUE, unload = TRUE)
}

update_pkgs <- gsub("^package:", "", loaded_pkgs)

install.packages(update_pkgs)
```

---

## Excursus: Updating packages

If you want to install a specific version of a package (not the most recent one), the easiest option is to use a function from the `remotes` package.

```r
library(remotes)

install_version("tidyverse", version = "1.3.0")
```

---

# [Exercise](https://jobreu.github.io/reproducible-research-gesis-2023/exercises/Exercise_Dependency_Management.html) time 🏋️‍♀️💪🏃🚴

## [Solutions](https://jobreu.github.io/reproducible-research-gesis-2023/solutions/Exercise_Dependency_Management.html)

---

# More comprehensive dependency management options in `R`

---

## `groundhog` vs. `renv`

See: https://groundhogr.com/renv/